1. Field of the Invention
The present invention relates to computerized time-lapse image analysis. More particularly, the present invention relates to (1) computerized mask edit guided processing method for efficient time-lapse image mask editing, (2) computerized track edit guided processing method for efficient time-lapse image track editing, and (3) computerized edit guided processing method for efficient time-lapse image mask and track editing.
2. Description of the Related Art
a. Description of Problem that Motivated Invention
The technology advancement has enabled the routine acquisition of movie (image sequences) from not only video cameras but also smart phones. Therefore, the demand for time-lapse (rather than fixed point) image analysis becomes more prevalent. In the bioscience field, the advent of time-lapse microscopy and live cell fluorescence probes has enabled biologists to visualize the inner working of living cells in their natural context. Expectations are high for breakthroughs in area such as cell response and motility modification by drugs, control of targeted sequence incorporation into the chromatin for cell therapy, spatial-temporal organization of the cells and its changes with time or under infection, assessment of pathogens routing into the cell, interaction between proteins, and sanitary control of pathogen evolution, etc. The breakthroughs could revolutionize the broad fields in basic research, drug discovery and disease diagnosis.
Deciphering the complex machinery of cell function and dysfunction necessitates a detailed understanding of the dynamics of proteins, organelles, and cell populations. Due to the complexity of the time-lapse image analysis tasks to cover the wide range of highly variable and intricate properties of biological material, it is difficult to have fully automated solutions except some dedicated high-volume applications such as cancer screening, wafer defect inspection. The first and the most critical step of time-lapse image quantification includes objects of interest mask detection and object tracking.
After tackling the huge complexities involved in establishing a live cell imaging study, scientists are often frustrated by the difficulties of image quantification that requires tedious manual operations or semi-automatic processing to achieve the objects of interest mask detection and object tracking. It is highly desirable to have smart editing methods that can efficiently create desired masks and tracks. Furthermore, it is desirable to have the edit results to improve automatic mask detection and object tracking results without the requirement of any image processing knowledge.
b. How Did Prior Art Deal with the Problem?
The prior art approach provides basic manual analysis tools or basic manual editing tools. However, the tools become impractical for time-lapse image analysis, as the data volume is high and the errors could accumulate over time. For example, in time-lapse image sequence tracking applications, a wrong track assignment in the early frame will propagate to the later frames. This causes significant inefficiency for a user to review and correct the mistakes, as the same mistakes have to be repeatedly corrected.
Furthermore, for a meaningful spatial-temporal analysis, the time-lapse image sequence has to cover a long time duration which has high data volume that requires the timely review and correction of analysis error or timely updates of the processing instructions (recipes) to achieve good outcome efficiently. The prior art tools do not satisfy the above requirements.